Teleoperation and System Health Monitoring
Mo-Yuen Chow, [email protected]
Advanced Diagnosis and Control (ADAC) LabDepartment of Electrical and Computer Engineering
North Carolina State UniversityRaleigh, NC 27695-7911
USA
Teleoperation and System Health Monitoring, Mo-Yuen Chow 2
Teleoperation Potential applications in NASA
Remote robotics manipulator control and teleoperation.E.g.: SPDM (Special Purpose Dexterous Manipulator).
Teleoperation and System Health Monitoring, Mo-Yuen Chow 3
Main challenge in teleoperation
Challenge Network delay during data transfer among sensors, controllers, and actuators
Challenge Network delay during data transfer among sensors, controllers, and actuators
What are the causes of network delaysBandwidth constraintMultiple packet transmissionsPacket lossPacket collisionEtc.
What are the causes of network delaysBandwidth constraintMultiple packet transmissionsPacket lossPacket collisionEtc.
Effect of network delaysSystem performance degradationSystem instability
Effect of network delaysSystem performance degradationSystem instability
Teleoperation and System Health Monitoring, Mo-Yuen Chow 4
Delays in teleoperation
Controller( )ku
Plant( )ty
( )ky T( )τsckkT −y
( )kr ( )τcakkT −u
Z.O.H
Network
τcak
τsck
τck
( )ty
( ) ( )k kTr r
( ) ( )k kTu u
( ) ( )k kTy y
Computational delayController-to-actuator delaySensor-to-controller delay
Time skew : k∆: Continuous system output: Discrete reference signal: Discrete control signal: Discrete system output
Teleoperation and System Health Monitoring, Mo-Yuen Chow 5
Gain Scheduler Middleware (GSM)
Gain Scheduler Middleware (GSM) is a novel methodology to utilize middleware to enable an existing non-network-based controller so they can be used for networked control.The proposed methodology applies middleware to modify the controller output with respect to the current network traffic condition in addition to providing appropriate network conditions.
Controller
Middleware gain scheduler
Networktraffic
estimator
Feedbackpreprocessor
Gainscheduler
NetworkRemotesystem
Probing
Controlsignal
Feedbacksignal
Teleoperation and System Health Monitoring, Mo-Yuen Chow 6
Optimal gain [K(i) surface] with respect to different curvature and time delay
( )A i ( )iτ
Opt
imal
K(i)
00.2
0.40.6
0.81
0
2
4
6
8
0
1
2
3
4
A
B
UV position X
UV position Y
path forthe UVto follow
vX
vY
J
v
τ
0 0 .2 0 .4 0 .6 0 .8 1 05
1 00
1 0
2 0
3 0
4 0
5 0
6 0
( )A i ( )iτ
( ) 0.1K i =
( )ˆ 1J i +
( ) 4K i =
( ) 1.4K i =
ε
Teleoperation and System Health Monitoring, Mo-Yuen Chow 7
Illustration: Tele-operation of an Unmanned Vehicle (UV)
The actual networked mobile robot is setup with the following configuration:
– The speed of both wheels are controlled by two PI controllers using a SK-515C microcontroller board.
– The path tracking controller and GSM are implemented on a notebook computer as RTLinuxprocesses.
– Data transfers between the tracking controller (or GSM) are delayed by an RTLinux process, using the internal hardware timer and delays from ADAC to KU.
– This is a scenario to focus specifically on delay effects and to avoid packet loss effects.
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Experimental results in virtual reality world
-0.5 0 0.5 1 1.5 2 2.5-0.5
0
0.5
1
1.5
2
2.5
x(m)
y(m
)
Robot tracks
No delayWith delay, no GS MWith delay and GS M
0 10 20 30 40 50 60 700
0.02
0.04
0.06
0.08
0.1
0.12
Time(s)
D(m
)
No delayWith delay, no GSMWith delay and GSM
0 10 20 30 40 50 60 700
0.02
0.04
0.06
0.08
0.1
0.12
Time(s)
D(m
)
No delayWith delay, no GSMWith delay and GSM
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Web-based remote access real-time Mechatronics Teleoperation Lab
PIDCONTROLLER
PROCESSor
PLANT
+-
COMMANDRESPONSE
PID controller wirelessunmanned vehicle
Use web browser to access the lab remotely.The lab server can communicate with the UV wirelesslyPID controller gains adjustment through wireless communication.
WEB browser LAB server
Unmannedvehicle
WEB cam
LAB server
WEB browser
Communicationnetwork
netmeeting
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System health monitoring
Invasive vs. non-invasive methodsModel-Based Paradigms
(Less suitable for system with highnonlinearity)
– Parameter estimation– Kalman filter
Model-Free Paradigms(More suitable for many real
world power engineering application)
– DSP– Expert system– Artificial Intelligence– Neural Networks– Fuzzy Logic– Hybrid Neural/Fuzzy Fault
Detection System
Fault Management
Module
Knowledge Engineering
(e.g.: Neural Network and Fuzzy Logic Technologies)
+
Domain Expertise(e.g. Shuttle Experts)
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Challenges
Millions and millions of dataData stream constantly flowing into the database from sensorsWorking environments are dynamics have many unexpected eventsNeed to make proper decision on Fault Detection, Diagnosis, Prognosis, {D,D,P} and Mitigation Control Actions in real timeNeed tools to:
– Provide meaningful information {D,D,P} by mining the million andmillion of data
– Able to react properly to unseen data of the dynamics operating environments
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Hybrid neural/fuzzy fault detection system
Pure NN Approach (Disadvantages)
– "Black-box" characteristics– Does not give heuristic
reasoningPure FZ Approach (Disadvantages)
– Difficult to Give an Exact Solution to the Problems
Hybrid NN/FZ System (Synergy)– Give Exact Solution
by Training Data
– Provide Valuable Information of the Fault Detection in a Heuristic Manner
GOOD FAIR BAD
TachometerMOTOR
ωΙΙ
Current Sensor
ω
Module 2
Module 1
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Immune system inspired {D,D,P} technologies
Natural immune system has distinguished features such as:
– Memory and learning;– Self/non-self discrimination;– Responding to unknown patterns.
These features form the bases of a new emerging research field, Artificial Immune Systems (AIS), and have been used in application areas such as:
– Anomaly/Fault Detection and Diagnosis;– Control;– Robotics.
What is our objective?Data miningIncorporate with NN/FZ system for {D,D,P}
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Artificial immune systems for motor fault detection, diagnosis, and prognosis
What is our objective?– Many motor incipient fault detection schemes are insufficient to discover and
identify new faults.– Our objective is to investigate the feasibility of developing an Intelligent Fault
Detection, Diagnosis and Prognosis System using Artificial Immune Systems on top of a Neural Network – Fuzzy Logic (NN-FZ) structure to actively detect, diagnose motor incipient faults and make an estimation of the remaining lifetime.
AIS algorithm
Neural-FuzzyIncipient faultdetection and
diagnosis
Motor systemmeasurement,
waveformanalyses and
featureextraction
Estimatedmotor
condition
Actual motorcondition (verified
at a later time)
Environmentalinformation
Motor faultdatabase
Teleoperation and System Health Monitoring, Mo-Yuen Chow 15
Biologically inspired intelligent fault diagnosis for power distribution systems
Distribution fault database(knowledge base)
(including district, spatialinformation, etc.)
Web geographicinformation
woodedarea
mall
house
Web weather channelgeographic weather
conditions
including protection devicestatus (e.g. fuse) and/orfault recorder waveforms
on-line distribution systemfault alarms
data miningand featureextraction
featureextraction
featureextraction
waveformanalyses
and featureextraction
Neural / Fuzzyfault root
causeidentification
estimateddistributionfault causes
actual faultcause (found
and verified at alater time)
update databasewith new data
AIS algorithmcontinuously guide
the NN-FZ system tolearn and absorb
new information toimprove its
performance
Teleoperation and System Health Monitoring, Mo-Yuen Chow 16
ADAC lab and research projects
Mechatronics and Control– Distributed Network Based Control and Applications– Distributed Network-Based Mobile Robots
(Unmanned Vehicles) control with Network QoS(Quality of Service) Constraints
– Adaptive Fuzzy Modulation for Network-Based PI Control (Completed)
Distance LearningDelta Project: Web-Based Remote Access Real-Time Mechatronics Laboratory Development
MotorsIntelligent Fault Detection Diagnosis and Prognosis of Induction MotorsDC Motor Acoustic Noise Analysis using Signal Processing TechniquesFast Prototype Motor System Simulation (MotorSIMII)A Neural/Fuzzy Approach for Motor Incipient Fault Detection (Completed)Incipient Fault Detection of Rotating Machines Using Neural Networks (Completed)
Teleoperation and System Health Monitoring, Mo-Yuen Chow 17
ADAC Lab and research projects – cont.
Power Distribution SystemsBiologically Inspired Intelligent Fault Diagnosis for Power Distribution SystemsPower Distribution Fault Location and Diagnosis (Completed)Distribution System Load Management (Completed) Power Quality Assessment of PowerDistribution Systems (Completed) Intelligent Energy Control (Completed)
NetworkingProactive Intelligent Network Fault Management (Completed)A Novel Set Theoretic Based Neural/Fuzzy Network Traffic Feature Extraction and Modeling Methodology (Completed)Communication System Network Control Software Performance Modeling and Fault Detection (Completed)
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